Recent advances in machine learning for electronic excited state molecular dynamics simulations

Brigitta Bachmair; Madlen Maria Reiner; Maximilian Xaver Tiefenbacher; Philipp Marquetand

doi:10.1039/9781839169342

Recent advances in machine learning for electronic excited state molecular dynamics simulations

Brigitta Bachmair, Madlen Maria Reiner, Maximilian Xaver Tiefenbacher, Philipp Marquetand

Publications: Contribution to book › Entry for reference work › Peer Reviewed

Abstract

Machine learning has proven useful in countless different areas over the past years, including theoretical and computational chemistry, where various issues can be addressed by means of machine learning methods. Some of these involve electronic excited-state calculations, such as those performed in nonadiabatic molecular dynamics simulations. Here, we review the current literature highlighting recent developments and advances regarding the application of machine learning to computer simulations of molecular dynamics involving electronically excited states.

Original language	English
Title of host publication	Chemical Modelling
Subtitle of host publication	Volume 17
Editors	Hilke Bahmann, Jean Christophe Tremblay
Publisher	Royal Society of Chemistry
Pages	178-200
Volume	17
ISBN (Electronic)	978-1-83916-935-9
ISBN (Print)	978-1-83916-741-6
DOIs	https://doi.org/10.1039/9781839169342
Publication status	Published - 19 Dec 2022

Austrian Fields of Science 2012

104022 Theoretical chemistry

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10.1039/9781839169342

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title = "Recent advances in machine learning for electronic excited state molecular dynamics simulations",

abstract = "Machine learning has proven useful in countless different areas over the past years, including theoretical and computational chemistry, where various issues can be addressed by means of machine learning methods. Some of these involve electronic excited-state calculations, such as those performed in nonadiabatic molecular dynamics simulations. Here, we review the current literature highlighting recent developments and advances regarding the application of machine learning to computer simulations of molecular dynamics involving electronically excited states.",

author = "Brigitta Bachmair and Reiner, {Madlen Maria} and Tiefenbacher, {Maximilian Xaver} and Philipp Marquetand",

note = "Manuelle Eingabe",

year = "2022",

month = dec,

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Recent advances in machine learning for electronic excited state molecular dynamics simulations. / Bachmair, Brigitta; Reiner, Madlen Maria; Tiefenbacher, Maximilian Xaver et al.
Chemical Modelling: Volume 17. ed. / Hilke Bahmann; Jean Christophe Tremblay. Vol. 17 Royal Society of Chemistry, 2022. p. 178-200.

Publications: Contribution to book › Entry for reference work › Peer Reviewed

TY - CHAP

T1 - Recent advances in machine learning for electronic excited state molecular dynamics simulations

AU - Bachmair, Brigitta

AU - Reiner, Madlen Maria

AU - Tiefenbacher, Maximilian Xaver

AU - Marquetand, Philipp

N1 - Manuelle Eingabe

PY - 2022/12/19

Y1 - 2022/12/19

N2 - Machine learning has proven useful in countless different areas over the past years, including theoretical and computational chemistry, where various issues can be addressed by means of machine learning methods. Some of these involve electronic excited-state calculations, such as those performed in nonadiabatic molecular dynamics simulations. Here, we review the current literature highlighting recent developments and advances regarding the application of machine learning to computer simulations of molecular dynamics involving electronically excited states.

AB - Machine learning has proven useful in countless different areas over the past years, including theoretical and computational chemistry, where various issues can be addressed by means of machine learning methods. Some of these involve electronic excited-state calculations, such as those performed in nonadiabatic molecular dynamics simulations. Here, we review the current literature highlighting recent developments and advances regarding the application of machine learning to computer simulations of molecular dynamics involving electronically excited states.

U2 - 10.1039/9781839169342

DO - 10.1039/9781839169342

M3 - Entry for reference work

SN - 978-1-83916-741-6

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EP - 200

BT - Chemical Modelling

A2 - Bahmann, Hilke

A2 - Tremblay, Jean Christophe

PB - Royal Society of Chemistry

ER -

Recent advances in machine learning for electronic excited state molecular dynamics simulations

Abstract

Austrian Fields of Science 2012

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